Method and system for computing path dependent probabilities of attaining financial goals

ABSTRACT

A method and system for computing the probability of attaining one or multiple financial goals is disclosed. Each goal is analyzed and reduced to a series of cash flows. A threshold criterion of success is established. A plurality of paths are generated. Each path is checked on the basis of the success criterion established earlier and those that do not meet the success criterion are considered failures. The probability of success is a function of the paths that are not failures.

FIELD OF THE INVENTION

The present invention relates to a method and system for utilizing acomputer to generate financial advice, and more particularly to a methodand system for determining statistical wealth projections and/orsimultaneous probabilities of meeting defined sets of financial goals.

BACKGROUND OF THE INVENTION

Rapid improvements in computer and communications technology,particularly developments of the Internet, have exponentially increasedan individual's ability to access financial data. Contemporaneouschanges in legal structures and financial vehicles have also increasedthe number of investment opportunities available to individuals. Thevast amount of financial data and investment opportunities, however,have made it difficult for an investor to determine which investmentvehicles will most likely enable the individual to meet his or herinvestment goals. Moreover, lifestyle and economic changes make itincreasingly necessary to prepare for multiple investment goals, such asretirement, educational needs and home ownership. Thus, there exists aneed for a method and system of generating financial advice whichenables an individual to determine the likelihood that the individual'sassets, future estimated savings, and investment plan will satisfy theindividual's investment goals.

Traditional systems attempt to predict wealth and/or the likelihood ofreaching a financial goal by computing the wealth of the user at sometime horizon. One method of predicting future wealth assumes a “fixed”rate of return, and possibly some volatility factor, across time forvarious asset classes. A calculation is then made to determine a normaldistribution of the user's terminal wealth. Multiple goals are handledby creating several phantom accounts, one for each goal. Each phantomaccount generating a different rate of return, often based on theinvestment horizon. This method, however, suffers from the drawback thatmost assets do not generate a “fixed” rate of return, and even thosethat provide so-called “fixed” returns (under common terminology) arenot guaranteed.

Other systems simulate a variety of conditions and then determine theprobability of success based on the percentage of wealth distributionbeing above the goal. For example, one system, offered by FinancialEngines, Inc., allows a user to input information, including a desiredretirement age and retirement income, a current age, a set of assetsindicated as taxable or non-taxable, and future estimated savings. Thesystem breaks down the user's portfolio to various asset classes, suchas stocks, bonds and cash. The system then reportedly simulates economicvariables over time, such as inflation, interest rates, and asset classreturns, and traces thousands of paths the user's portfolio might takeuntil the retirement age is met. After translating each scenario into anannuity and adding any other retirement benefits, the system looks atthe terminal value of wealth attained and tallies the number ofscenarios that do and do not reach the user's retirement goal (expressedin a dollar amount per year).

These systems, however, fail to simulate certain real life thresholdconcerns, and fail to optimize for multiple goals. One significanteffect of this is to overstate the desirability of an aggressiveportfolio in certain situations. For example, the mean of wealth atperiod T is typically calculated in analytic systems as e^(μT) ore^((μ+1/2σ) ² ^()T) depending on the assumptions made. However, thenumber of paths that satisfy all intermediate goals and thresholds maydecrease with increased volatility. Thus, for an individual having acurrent portfolio that is close to the individual's threshold, anaggressive, high volatility, portfolio may lead to a number of scenariosin which the portfolio value dips below the threshold—essentiallycausing a failure. These systems also fail to optimize for individualgoals having multiple cash flows across time. Thus, there is a need fora multi-period, path dependent analysis system which optimizes analysisfor multiple goals over multiple-periods of time and which accounts forintermediate threshold concerns.

SUMMARY OF THE INVENTION

The present invention addresses these and other deficiencies of theprior art by providing a system and methodology for computing theprobability of attaining one or multiple goals. Each goal is analyzedand reduced to a series of probabilistic cash inflows or outflows. Thesecash flows are time ordered and combined to provide an integratedpicture and to enable a computation that allows for the simultaneousachievement of multiple goals. A threshold criterion of success isestablished which may be supplied by the user or generated by thesystem, e.g. based on prudent financial planning such as always having acertain minimum amount of money. A plurality of paths, or samples aregenerated preferably using Monte Carlo sampling, to ascertain anaccurate distribution of future wealth. Each path is checked (for a setof periods or time steps) on the basis of the success criterionestablished earlier, and those that do not meet the success criterionare considered failures. The probability of success is a function of thepaths that are not failures.

Because the computation is path dependent, the system can account forthe effect that cash spent in attaining a goal in the early years willhave on the probability of reaching another goal in the later years.Losses in the early years adversely affect the probability of attaininggoals set for the later years. The flexibility of the success criterionallows different users to have different criterion of success dependingon their risk preferences.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a system block diagram showing the components of anintegrated system utilizing the present invention;

FIG. 1B illustrates an example of a typical computer system upon whichan embodiment of the invention may be implemented;

FIG. 2A shows a simulated time line of market values a user's portfoliomay take over time;

FIGS. 2B and 2C show methods for calculating goal probabilities forsimulated scenarios as a function of the paths that are not failures inaccordance with the invention;

FIG. 2D is an example of a series of cash flows;

FIG. 2E shows a sample statistical wealth projection;

FIG. 2F shows a sample goal probability calculation in accordance withthe invention;

FIG. 2G shows an example of multiple overlapping goals;

FIG. 2H shows a screen display of showing a plurality of cash inflowsand outflows;

FIG. 3 is a high level flow chart showing an embodiment which dividesassets as taxable or tax favored and categorizes assets by asset type;

FIG. 4 is a high level flow chart showing a method for collapsing assetsinto asset pools and calculating probabilities of meeting a defined setof goals in accordance with one embodiment of the present invention;

FIG. 5 is a flow chart showing a method of collapsing assets into pools;

FIG. 6 is a flow chart showing a method of generating random returns;

FIG. 7 is a flow chart showing a method of performing Monte Carlosimulation;

FIG. 8A is a flow chart showing a method of calculating income tax inaccordance with the present invention;

FIG. 8B is a flow chart showing a method of calculating cash flows formultiple asset pools in accordance with the present invention;

FIG. 8C is a flow chart showing a method of adjusting for realizedcapital gains;

FIG. 8D is a flow chart showing a method of adjusting for realizedcapital gains due to management turnover; and

FIG. 9 is a flow chart showing a method of computing goal probabilitiesin accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1A depicts the various modules and interfaces of an integratedsystem embodying the present invention. The system is preferablyimplemented as an Internet site accessible running on a financial server(55, FIG. 1B) accessible via the World-Wide-Web (57, FIG. 1B). A userwill typically enter the site amongst other purposes to seek advice onone or more issues, for example, saving for retirement, a home purchaseor a child's education. The user may access the site through a userworkstation (58, FIG. 1B). The user is prompted to enter relevantinformation about their portfolio by a portfolio module 10. Thisinformation may include, for example, current regular income, savings,asset classes, expenses, and cash flows associated with their financialgoals. The system may also prompt the user to input expectations forgrowth of those cash flows or, alternatively, the system will generateestimated cash flow growth.

The user's asset holdings are input to an advice engine 20 whichconverts the goals into a series of cash flows, simulates future wealth,analyzes the probabilities of meeting the multiple goals, and providesportfolio analytics. User information is stored on a database (56, FIG.1B). If the user elects to execute one or more recommendations, atransaction order may be generated and forwarded to a brokerage module30 which executes the order and reports back to the portfolio module 10which adjusts the user's assets mix. Alternately, the user may elect tobe recommended to a referral network 40 to communicate with a brokeragecommunity. The user may also wish to have direct access to on-lineresearch 50 while analyzing the portfolio.

FIG. 2A shows a simulated time line showing various paths or scenarios(P1, P2, P3, P4 & P5) a user's portfolio market value (x) may take undervarious simulated conditions over a time period (k=1 to n). FIG. 2Ashows two goals, an intermediate goal (x_(gl)) at time k=2 and a finalgoal (x_(gf)) at time k=n. A goal may consist of multiple cash flowsover multiple periods (not shown), such as, for example, paying forcollege tuition for a child. Five simulated paths are depicted (P1–P5).A threshold success criterion x_(s) is also shown. When the simulatedmarket value dips below this threshold the path is considered a failure.Each path starts off with an initial asset value and simulates the valueacross time periods.

As shown, the market value of each path is reduced by the value of theintermediate goal X_(gl), at time k=2. Thus, the simulation takes intoaccount the effect that cash spent in earlier years has on attaininggoals in later years. Path P4 has a final value at period k=n which isless than the final goal x_(gf) and would be considered a “failure.”Paths P1–P3 and P5 have final values x1_(n)–x3_(n) and x5_(n) which aregreater than the final goal x_(gf). However, paths P2, P3 and P5 dipbelow the success criteria at least one point along the time line andwould also be considered “failures.” For example the market value of thepath P3 dips below the success criteria x_(s) at period k=n−4; themarket value of path P2 actually goes below zero at period k=n−2; and P5does not have sufficient funds to satisfy intermediate goal x_(gl) attime k=2 without going below the success criterion. Thus, the onlysimulated path satisfying the intermediate and final goals (x_(gl) andx_(gf)), and success criterion (x_(s)), is P1.

As noted above, a goal may consist of multiple cash flows. In anadvantageous embodiment, the probability is calculated at the end ofeach period, but the probability, using the binary of failure/success atany time before, at the last cash flow period corresponding to the goalbecomes that goal probability, i.e. it is the probability of achievingthat goal subject to achieving all, or a chosen set of, the cash flowsbefore it.

FIG. 2A also shows a distribution of wealth projections at period k=n.This represents the projected wealth of the user at the end of thesimulation. In an advantageous embodiment, the distribution may becalculated at the end of each goal, e.g. k=2.

FIGS. 2B–2C show exemplary methods of calculating goal probabilities inaccordance with the invention. FIG. 2B shows a binary method forcalculating goal probabilities in which a path is assigned a goalprobability of 1 if it satisfies all goals and the success criterion foreach period, and 0 if it fails to satisfy every goal or the successcriterion for every period. Thus, for the simulation shown in FIG. 2B,path P1 has a goal probability of 1. Paths P2–P5 are assigned goalprobabilities of 0. Those probabilities may be averaged to give anoverall probability of 1 in 5 of satisfying the intermediate and finalgoals and success criterion.

FIG. 2C shows an application of one type of decaying goal probabilityfunction with a limited “memory.” Each path is assigned a goalprobability equal to (the sum (for i=0 to 3) of delta_(n-i)/(i+1)²)divided by (1/1+1/4+1/9+1/16), where delta_(i) is a function of whetherthe market value for the path at period (i) satisfies the successcriteria. In the example shown, delta_(i) equals 1 if the market valueof period (i) satisfies the success criterion and 0 if it does not.Thus, the goal probability of path P1=1;P2=(1/1+1/4+0/9+0/16)/(1/1+1/4+1/9+1/16); P3=1; andP4=(0/1+1/4+1/9+1/16)/(1/1+1/4+1/9+1/16). Those goal probabilities maybe averaged.

Other mechanisms for assigning goal probabilities will be readilyapparent to those of ordinary skill in the art based on the disclosureherein. For example, one may have a decaying function without a“memory.” One may define any path which can not satisfy a final and/orintermediate goal as having zero goal probability. One may assigndifferent intermediate numbers depending upon whether a path fails tosatisfy a success criterion, as compared to having negative wealth. As afurther example, one may assign any of a number of types of deltafunctions.

FIGS. 2D, 2E and 2F show an exemplary series of cash flows, and aresulting wealth projection and goal probability, respectively. FIG. 2Dshows a series of planned incoming cash flows of $150,000 for years 1–7,9 and 10, and a series of outflow goals for years 8, 11–30. A simulatedwealth projection is shown in FIG. 2E, based on an initial set of assetsand the cash flows of FIG. 2D. In a preferred embodiment, the systemdisplays the median wealth value, 17^(th) percentile wealth value and83^(rd) percentile wealth value. As described below, the wealthprojection values are preferably simulated using Monte Carlo simulation.The simulated market values are used to determine the probability thatthe user will achieve her financial goals (e.g. cash outflows for years8, 11–30).

FIG. 2G shows an exemplary series of cash inflows 70, and three goals71, 72 and 73. Goals 71 and 72 each consist of multiple-period cashflows. Goal 73 is a single cash flow that overlaps with goal 72. Successfor multiple-period cash flow goals may be defined as meeting the cashflow needs for each period. Thus, for example, for a simulated path tosatisfy goal 72 it would have to have sufficient funds to make cashoutflows for each of the six periods.

FIG. 2H shows a plurality of cash inflows (regular income, retirementincome, other income) and a plurality of cash outflows (living expensesand event driven expenses). FIG. 2H also shows the effect of taxes onthe wealth projection.

With reference to FIG. 3, an overview of an advantageous embodimentwhich divides assets into taxable and tax favored classes andcategorizes assets by asset group (e.g. large cap, small cap,international, biotech, high technology, etc.) is discussed.

The user inputs (step 60) the parameters for one or more financial goalsinto the system. This consists of one or more dollar amounts atspecified time frames. These financial goals are converted to cashflows. The user next inputs (step 61) a portfolio of assets. These maybe individually added or accessed from one or more links such as via theWeb. These variables may include, for example, the market values oftaxable and tax exempt assets, the leverage value for taxable and taxexempt assets, and the book value of taxable and tax exempt asset pools.In one advantageous embodiment, the system categorizes (step 62) eachasset by the type of financial asset, such as international stock, smallcap stock, large cap stock, cash, bonds, etc. This is designed to savecomputation time by treating similar assets similarly. Depending on thetype of client (e.g. individual or institutional), assets may also beadvantageously categorized as taxable or tax favored (step 63) (eithertax exempt or tax deferred). Present and expected cash flows areinputted or generated (step 64). These include, for example, expectedreturns for taxable and tax exempt asset pools and individual assets.The cash flows are aggregated on a period by period basis (step 65).Criteria for success are defined (step 66).

Any number of different criteria of success can be establishedreflecting the different risk tolerances of the investor. “Absolute”thresholds have some arbitrary value, such as zero, $100,000 or−$100,000. “Relative” thresholds may be defined as some value which is afunction of some variable such as inflation or current earnings. Forexample, a relative threshold could be six months future estimatedliving expenses, or a percentage of future estimated wages. Alternately,a multi-period success criterion may be used including, by way ofexample, falling below an arbitrary level or falling below some levelfor more than one period. This level may be allowed to vary over time.Success criteria may further be a function of leverage levels. Successcriteria may further be defined as having “memory” or a “decayingmemory.” For example, with an “absolute with memory” criterion, a paththat dips below the threshold is considered a failure for all subsequentperiods. Examples of decaying memory thresholds are described below.

The probability of achieving the goals is determined (step 67).Additionally, the system may plot out an expected distribution of wealthfor each period.

With reference to FIG. 4, the process of collapsing assets into assetpools, and simulating probabilities of attaining goals, in accordancewith one embodiment of the invention is described as follows:

-   -   (step 100) collapse the given asset data into two or more pools        (e.g. a taxable pool, a tax favored pool, or a concentrated        positions pool);    -   (step 200) generate random returns;    -   (step 300) perform path dependent, multi-period Monte Carlo        simulation using the above random return data for each of the        two pools, to calculate net wealth for each period; and    -   (step 400) determine future wealth and goal probabilities for        each period, and generate the output statistics.

As noted above, other methods for simulating net wealth for each periodmay be used in practicing the invention.

With reference to FIG. 5, the process of collapsing assets (step 100)into multiple pools is preferably implemented as follows. All of thegiven assets in the portfolio are categorized either as taxable or taxdeferred assets. All assets belonging to one category are collapsed intoa single asset pool having a single expected return, volatility andyield. The process described below assumes a two pool system: a taxablepool and a tax favored pool. As noted above, the tax favored pool maycomprise tax exempt and/or tax deferred assets. For the purposes of thedescription herein, the terms tax favored, tax deferred and tax exemptare used interchangeably, unless noted otherwise. Additional, and/ordifferent, pools, such as a separate pool for one or more concentratedpositions, may be used. The expected return, volatility and yield forthe collapsed asset pool are calculated as follows:

Calculate (step 101) the leverage value for the tax and tax exemptpools. Leverage is the amount of money borrowed in connection with theportfolio. Leverage allows the portfolio to take negative values. In anadvantageous embodiment, only one asset, such as US cash, is allowed tohave negative market value in each of the asset pools.

Convert (step 102) the annualized expected return and volatility of theasset pools into continuously compounded values. Calculate (step 103)the relative weights of taxable and tax favored pools. This may becalculated as a function of the sum of the market values of each assetpool. Calculate (step 104) the expected return of each pool. This may becalculated as the weighted mean of all the assets in each pool withpositive market values.

The correlation of the pools is calculated (step 106) using a definedset of weight vectors (step 105). The management fee for the asset poolsis calculated (step 107), preferably as the weighted (by market values)average of the management fee of the individual assets in that pool.

The yield for the asset pools is calculated (step 108) as the weighted(by market values) average of the individual asset yield values of theassets in that pool. This yield is annualized value.

The system calculates (step 109) the B/M ratio of the asset pool. Thismay be calculated as the ratio of the sum of market values of the assetsto the sum of book values of the assets in that pool.

The management turnover for the tax favored pool is assumed to be zeroand for taxable asset pool management turnover is estimated as theturnover for each asset class (step 110).

With reference to FIG. 6, a preferred method for generating randomreturns (step 200) and simulating multiple paths (step 300) is nowdescribed. Those returns are used to simulate correlated random walksfor each pool. Two pools of uncorrelated Gaussian random vectors withzero mean and unit variance are generated (step 201), which are thentransformed to reflect the mean variance and correlation of each pool.The required volatility and expected return values for each pool iscalculated (step 203) based on the type, and concentration, of assets inthe pool. The system then simulates (step 204) multiple paths, each ofwhich is one realization of a period of time (k=1−n) that is consistentwith the calculated volatility and expected return.

With reference to FIG. 7, the preferred method of utilizing a pathdependent, multi-period Monte Carlo simulation (step 300) is described.As noted above, alternate simulation techniques may be used inpracticing the invention. Thus, the methodology described herein may beused with any asset projection system, as long as wealth value for eachperiod is known for each scenario. For example, instead of a Monte Carlotechnique, the system may vary individual parameters, such as equity(high vs. low return) or inflation (high vs. low).

The purpose of simulation step (step 300) is to track how much moneyeach pool would have. In the period-by-period simulation, the wealthcomputation is based on inflows and outflows, and the amount in eachpool. The system preferably tracks how much tax a user would have topay, how many assets would have to be liquidated, etc.

In order to save computation time, an advantageous embodiment furthergroups assets into multiple asset classes or pools such as large cap,small cap, etc. However, the invention could be practical using a singleclass or no classes, in which case assets are examined individually.

The capital gains rate is calculated (step 301) as the expected(treated) return rate−yield. Net income after income taxes andmanagement fees is calculated (step 302). With reference to FIG. 8A, apreferred method for performing income tax calculations is described.Income tax is calculated for the taxable asset pool (step 701). Incomefrom the taxable asset pool is calculated (step 702) as the initialmarket value*(the yield from taxable asset*(1−tax rate)−the managementfee). Accumulated capital gains are initialized (step 702) as zero forthe first year and as the initial accumulated capital gains for otherperiods. Net return after income tax and management fee is calculated(step 704) as rate of return minus income taxes/initial mktvalue−management fee.

For the tax exempt pool, income tax is set (step 705) to zero. Incomefrom the tax exempt asset pool is calculated (706) as the initial marketvalue*(the yield from tax exempt asset−the management fee). Accumulatedcapital gains for the tax exempt asset pool is initialized (step 707).Net return after income tax and management fee is calculated (step 708)as the rate of return−income taxes/initial mkt value−the management fee.

With continued reference to FIG. 7, cash flows for the taxable andnon-taxable pools are calculated (step 303). With reference to FIG. 8B,a preferred method for performing cash flow calculations is described.Cash outflow from the taxable asset pool is the maximum amount of cashoutflow that is possible without making its market value negative, orthe actual cash outflow required if this amount is smaller than themaximum outflow that is allowed.

For the taxable pool, total cash flow required is calculated (step 720)as cash inflow into the taxable asset pool−cash outflow from the taxablepool. If this value is negative (step 721), then the maximum amount ofcash that can be taken out from the taxable pools is calculated (step722).

For the tax exempt pool, if all the cash outflow required can beaccommodated from the taxable pool, cash flow is equal to the cashinflow into the tax exempt pool for that period (step 723). Otherwisecash outflow for the tax exempt pool is equal to this remaining amount(step 721).

With continued reference to FIG. 7, realized capital gains after cashflow are calculated (step 304). With reference to FIG. 8C a preferredmethod of calculating realized capital gains, and adjusting othervariables, is described. For the taxable asset pool, realized capitalgains due to cash flow are computed (step 720), market value afterpersonal cash flow is adjusted (step 731), book value after personalcash flow is adjusted (step 732), B/M ratio after personal cash flow isadjusted (step 733), capital gains taxes after cash flow is adjusted(step 734), return after cash flow is adjusted (step 735), andaccumulated realized capital gains after cash flow is adjusted (step736).

For the tax exempt pool, only market value is of interest. Market valueafter cash flow for the tax exempt pool is adjusted (step 737). Capitalgains after cash flow, and return after cash flow, are set.

With continued reference to FIG. 7, realized capital gains due tomanagement turnover are calculated (step 305). With reference to FIG. 8Da preferred method for performing this calculation and adjusting othervariables is described.

For the taxable pool, realized capital gains due to management turnoveris calculated (step 740) as (1-book/market ratio after client cashflow)*market value after cash flow*managing turnover; market value aftermanaging turnover is adjusted (step 741) as the market value afterclients cash flow−capital gains tax paid; book value after managementturnover is adjusted (step 742) as the book value after cashflow+realized capital gain after cash flow; B/M ratio is adjusted (step743); capital gains taxes (step 744), return is adjusted (step 745), andaccumulated capital gains (step 746) are adjusted.

Since managing turnover for the tax exempt pool is taken as zero, theabove calculations are not necessary for the tax exempt pool.

With continued reference to FIG. 7, net wealth for the period iscalculated (step 306). For the case when there is no leverage, net worthof the portfolio during period (k) is the sum of the market value aftermanagement turnover for the taxable assets plus the market value afterthe client's personal cash flow for the tax exempt pool (since themanagement turnover for the tax exempt pool is taken as zero). If thenet wealth for a period is negative, then for the subsequent periods theportfolio is not evolved using the random returns. Instead, the marketvalues are set to zero and the net wealth is added to the leverage.

In the case where there is leverage, the leverage is evolved at amultiple of the US cash return rate. This process is continued until thetotal leverage plus the total cash flow turns positive. When this ishappens, the cash flows are reduced by the leverage amount, the leveragevalue is set to zero and the portfolio is evolved normally. Thus, forthe case where there is leverage, net wealth for the period iscalculated as the market value after managing turnover for the taxablepool, plus market value after the client's personal cash flow for thetax exempt pool, plus the total leverage for the period.

With reference back to FIG. 4, after performing Monte Carlo simulationto determine net worth for each period (k), goal probability for eachperiod is computed. In the preferred embodiment goal probability iscomputed as follows. First, a delta function for each period (k) (step801) for each observation (j) (step 802) is computed (step 803).Preferably the delta function is equal to one if net worth satisfies thesuccess criterion and zero if not.

Next, the goal probability for each period (k), for each observation(j), is calculated (step 804) as a function of whether the client metthe success criterion for prior periods (k−i). In the followingadvantageous formula, a decaying four year memory for negative net worthpaths is used. This version does not “penalize” a client's current goalprobabilities (ie., decrease the calculated probability that the clientwill achieve goals) for having had a negative net worth more than fouryears ago. As will be apparent to those of skill in the art, otherfunctions or values may be used.

$g_{{pk}_{j}} = \frac{\sum\limits_{i = 0}^{3}\;\frac{{{\,^{\delta}p}\left( {k - i} \right)}j}{\left( {i + l} \right)^{2}}}{\sum\limits_{i = 0}^{3}\frac{1}{\left( {1 + i} \right)^{2}}}$

Goal probability for a period (k) is averaged (step 805) over a numberof observations (j). Preferably, this is over n=400 observations. Thismay be increased, for concentrated positions, or if the volatility ishigh. This may be expressed as:

$g_{pk} = \frac{\sum\limits_{j = 1}^{400}\; g_{{pk}_{j}}}{400}$

Although the present invention was discussed in terms of certainpreferred embodiments, the description is not limited to suchembodiments. Rather, the invention includes other embodiments includingthose apparent to a person of ordinary skill in the art. Thus, the scopeof the invention should not be limited by the preceding description butshould be ascertained by reference to the claims that follow.

1. A method, with the aid of a digital computer, of determining theprobability a user will achieve at least one financial goal, comprising:the computer identifying a set of assets for said user, said assetsassociated with a market value; the computer establishing a criterionfor success for said user, the criterion for success providing at leastone predetermined market value reference associated with at least oneperiod; the computer simulating a plurality of market scenarios on saidassets, each said scenario adjusting said market value of said assetsfor a plurality of selected periods; the computer applying predeterminedcash outflows for each of said plurality of periods for each saidplurality of market scenarios; the computer determining for at least onesecond period, for each said scenario, whether said market value duringsaid at least one second period satisfies said criterion for successassociated with said period; and the computer eliminating any scenariowhere said market value does not satisfy said criterion for successduring said second period.
 2. The method of claim 1, further comprising:the computer calculating the probability said user will achieve said atleast one financial goal, said calculated probability being a functionof the number of non-eliminated simulated market scenarios that satisfysaid criterion for success.
 3. The method of claim 2 wherein said atleast one second period comprises each of said first plurality ofperiods.
 4. The method of claim 2 wherein said at least one secondperiod comprise a predetermined number of periods of said firstplurality of periods, whereby periods which do not satisfy said successcriterion more than said predetermined number of periods before a finalperiod do not decrease said calculated probability.
 5. The method ofclaim 2 wherein said calculated probability comprises a decayingfunction.
 6. The method of claim 5 wherein said calculated probabilitycomprises a decaying function based on a predetermined set of periods.7. The method of claim 1 further comprising: the computer computing anexpected distribution of wealth based on said plurality of scenarios. 8.The method of claim 1 further comprising: the computer categorizing saidassets by asset type, said categorization creating a plurality of assetgroups, said simulation of market scenarios being applied on an assetgroup basis, whereby all assets within a group are treated identically.9. A method, with the aid of a digital computer, of determining theprobability that a financial goal expressed as a cash outflow will bemet, comprising: (a) the computer identifying a set of assets, saidassets associated with a market value; (b) the computer establishing acriterion for success, said criterion for success associated with aplurality of cash outflows over a plurality of periods; (c) the computersimulating a plurality of market scenarios on said assets, each saidscenario adjusting said asset market value of said assets for each saidperiod; (d) the computer eliminating a scenario if a correspondingcriterion for success is not met during a predetermined number of saidplurality of said periods; and (e) the computer calculating theprobability said criterion for success will be satisfied by reference toany remaining non-eliminated scenarios.
 10. The method of claim 9,wherein said criterion for success is an absolute criterion.
 11. Themethod of claim 10, wherein said criterion for success has a memory. 12.The method of claim 10, wherein said criterion for success has adecaying memory.
 13. The method of claim 9, wherein said criterion forsuccess is a relative criterion.
 14. A computer system for determiningthe probability that a financial goal expressed as a cash outflow willbe met, comprising: (a) a database including: (i) a set of assetsassociated with a user, said assets associated with a market value; and(ii) a criterion for success associated with said user, said criterionfor success associated with a plurality of periods; and (b) a programmedprocessor configured to: (i) simulate a plurality of market scenarios onsaid assets, each said scenario adjusting said market value of saidassets for each said period; (ii) determine whether a market valueduring a period satisfies said criterion for success associated withsaid period; (iii) eliminate any scenario if the market value does notsatisfy said criterion for success during a predetermined number of saidperiods; and (v) determining the probability that a particular cash flowwill be met by reference to any remaining non-eliminated scenarios. 15.The computer system of claim 14 wherein, said database includes aplurality of financial goals associated with said user; said processoris configured to convert said plurality of financial goals into cashflows; and said simulation of a plurality of market scenarios on saidassets includes applying said cash flows to said adjusted market valuesduring each corresponding period.
 16. The computer system of claim 14wherein said criterion for success varies for each said period of saidplurality of periods.
 17. The computer system claim 14 wherein saidcriterion for success varies for each said period of said plurality ofperiods associated with said criterion.
 18. The computer system claim 14wherein said processor is further configured to: receive said cashoutflow associated with said plurality of financial goals; and determinethe statistical probability that said cash outflows will be satisfied ona periodic basis.